研究动态
Articles below are published ahead of final publication in an issue. Please cite articles in the following format: authors, (year), title, journal, DOI.

基于机器学习策略的肝内胆管癌的表观基因组全面发展和验证了一种预后甲基化评分方法。

Epigenome-wide development and validation of a prognostic methylation score in intrahepatic cholangiocarcinoma based on machine learning strategies.

发表日期:2023 Aug 01
作者: Xing Chen, Liangqing Dong, Lu Chen, Yuan Wang, Jinpeng Du, Lijie Ma, Xiaokai Yan, Jiwei Huang, Mingheng Liao, Xiangzheng Chen, Dongming Liu, Jin Li, Bo Zhang, Wen Teng, Kefei Yuan, Deqiang Sun, Qiang Gao, Yong Zeng
来源: Epigenetics & Chromatin

摘要:

临床参数为基础的判断图表和分期系统在预测肝内胆管癌(ICC)患者的生存中提供有限的信息。在本研究中,我们开发了一种甲基化标记,能精确预测手术后的总体生存(OS)。我们在中国的三个肝胆中心的两个独立队列(发现队列,n=164;验证队列,n=170)中进行了基于全基因组亚硫酸盐测序(WGBS)的DNA甲基化的表观基因组研究。通过参考差异甲基化区域(DMRs),我们提出了预后性甲基化区域(PMRs)的概念,其中包括连续预后性甲基化CpGs(PMCs)。基于机器学习策略(随机森林和最小绝对收缩和选择回归),在发现队列中构建了一个基于14个PMRs的预后性甲基化评分(PMS),并在验证队列中得到验证。预测整体生存的PMS的C指数在发现队列和验证队列中分别为0.79和0.74。在整个队列中,PMS是生存的独立预测因子(风险比(HR)=8.12;95%置信区间(CI):5.48-12.04;P<0.001),而PMS的C指数(0.78)显著高于约翰·霍普金斯大学医学院(JHUSM)的判断图表(0.69,P<0.001)、东方肝胆外科医院(EHBSH)的判断图表(0.67,P<0.001)、美国肿瘤协会(AJCC)肿瘤-淋巴结-转移(TNM)分期系统(0.61,P<0.001)和MEGNA预后评分(0.60,P<0.001)。PMS的第四分位数的患者可以从辅助治疗(AT)中获益(HR=0.54;95%CI:0.32-0.91;log-rank P=0.043),而第一至三分位数的患者则无法。然而,其他判断图表和分期系统却做不到这一点。对潜在机制的进一步分析表明,PMS与肿瘤生物行为、通路激活和免疫微环境有关。PMS可以提高ICC患者预测的准确性,并识别出能从AT中获益的患者,有可能促进对ICC患者的治疗决策。2023年 肝胆外科与营养。保留所有权利。
Clinical parameter-based nomograms and staging systems provide limited information for the prediction of survival in intrahepatic cholangiocarcinoma (ICC) patients. In this study, we developed a methylation signature that precisely predicts overall survival (OS) after surgery.An epigenome-wide study of DNA methylation based on whole-genome bisulfite sequencing (WGBS) was conducted for two independent cohorts (discovery cohort, n=164; validation cohort, n=170) from three hepatobiliary centers in China. By referring to differentially methylated regions (DMRs), we proposed the concept of prognostically methylated regions (PMRs), which were composed of consecutive prognostically methylated CpGs (PMCs). Using machine learning strategies (Random Forest and the least absolute shrinkage and selector regression), a prognostic methylation score (PMS) was constructed based on 14 PMRs in the discovery cohort and confirmed in the validation cohort.The C-indices of the PMS for predicting OS in the discovery and validation cohorts were 0.79 and 0.74, respectively. In the whole cohort, the PMS was an independent predictor of OS [hazard ratio (HR) =8.12; 95% confidence interval (CI): 5.48-12.04; P<0.001], and the C-index (0.78) of the PMS was significantly higher than that of the Johns Hopkins University School of Medicine (JHUSM) nomogram (0.69, P<0.001), the Eastern Hepatobiliary Surgery Hospital (EHBSH) nomogram (0.67, P<0.001), American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging system (0.61, P<0.001), and MEGNA prognostic score (0.60, P<0.001). The patients in quartile 4 of PMS could benefit from adjuvant therapy (AT) (HR =0.54; 95% CI: 0.32-0.91; log-rank P=0.043), whereas those in the quartiles 1-3 could not. However, other nomograms and staging system failed to do so. Further analyses of potential mechanisms showed that the PMS was associated with tumor biological behaviors, pathway activation, and immune microenvironment.The PMS could improve the prognostic accuracy and identify patients who would benefit from AT for ICC patients, and might facilitate decisions in treatment of ICC patients.2023 Hepatobiliary Surgery and Nutrition. All rights reserved.